> For the complete documentation index, see [llms.txt](https://megap.gitbook.io/megap/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://megap.gitbook.io/megap/readme.md).

# Bridging the MEG Gap

Traditional MEG pre-processing workflows often feel fragmented, time-consuming, and challenging to standardize, particularly when working with large datasets. MEGAP (MEG Automatic Pipeline) offers a seamless solution to these challenges. By automating tasks such as noise removal, artifact correction, and data standardization, MEGAP transforms raw MEG data into clean, consistent, and ready-to-use outputs. It addresses common limitations like manual parameter adjustments, incomplete artifact management, and outdated filtering techniques, providing researchers with an efficient and modern approach to MEG analysis.

With MEGAP, pre-processing becomes faster, more reliable, and easier to replicate—unlocking the true potential of your MEG data. Welcome to a new era of MEG pre-processing!